Intelligent Data Management Supports Better Data Access

Data access is a right, not a privilege. That philosophy may not be directly stated by companies, but certainly they have moved in that direction.

Of course, it’s understood that not everyone is entitled to access all data. Nonetheless, the landscape has changed from the days when IT primarily held the keys and access was granted exclusively to Data Scientists for strategic analysis and Business Analysts who bridged the gap between IT and the business. Now, line-of-business personnel are in the mix, using Self-Service Analytics for querying and report generation.

Join that trend with the phenomenal growth in data from multiple sources in multiple formats and hosted across multiple venues (like the Cloud), and you can see how barriers to entry can expand for the increased number of users that want or need to get to data sets. According to a Dun & Bradstreet survey, three fifths of executives at 870 companies said their data is stored in silos, and only 20% reported that their data is fully integrated and leveraged across the company.

“The real organizational problem is how do I make people able to discover the right data, to make sure it’s what they really want, and make it available to them?” says Ronen Schwartz, Senior Vice President and General Manager, Big Data, Cloud, and Data Integration at Informatica. Schwartz says this problem of discovering and accessing the correct data “is a magnitude of order more complex with more data and more types of users.”

To expose and maximize the value of information assets in increasingly complex environments, businesses look to Metadata Management solutions. The market for these tools was valued at $2.72 billion in 2017 and is expected to grow at a CAGR of 22.6% during the forecast period – from 2018 to 2023 — to reach 9.34 billion, according to the Global Enterprise Metadata Management Market Report. Over 2,000 customers already use Informatica’s Metadata Management product for Enterprise Data Integration, Data Quality, and Data Cataloging, Schwartz says. It recently was positioned as a Leader in Gartner’s 2018 Magic Quadrant for Metadata Management Solutions.

To further its efforts at Intelligent Data Management, Informatica has been focusing on new capabilities for its Master Data Management (MDM), Intelligent Cloud Services, and Data Privacy and Protection products across hybrid, multi-cloud, and on-premises environments. Recently the company announced:

New MDM capabilities including improved dashboards, metrics, security, and access aligned by business users and roles;

Updates for its Intelligent Cloud services that include high-performance Data Integration for Data Warehousing;

Data Privacy and Data Protection also features new capabilities that link subject identities to sensitive data with continuous intelligence, such as the location of data stores.

Bringing Data Together

Addressing the company’s perspective on Metadata Management, Schwartz says that encapsulates multiple layers of technology. Scanners collect metadata associated with on-premise apps, Cloud apps, databases of all types, Informatica’s own integration environments, and competitor environments. That aids exposure of data in the Data Catalog and one-click access for authorized users based on security, privacy, and other requirements. If direct access is not possible, Informatica solutions can trigger a process to ask the right individuals for access to that data. Artificial Intelligence and Machine Learning work for metadata enrichment, generating additional metadata. The AI and Machine Learning processes help in profiling data and comparing data sets against each other to see which is more complete.

One of the largest customers leveraging this is a leading car manufacturer, which has undertaken a massive organizational change to bring different divisions – along with their data – together in order to speed innovation around concepts like connected cars and autonomous driving.

Barriers need to be knocked down across business groups for success, so the auto manufacturer is using the Data Catalog to build advanced Data Governance and Data Discovery mechanisms that will let people from different departments and groups identify the right data, share it, improve it, and collaborate with their peers, Schwartz says:

“They are seeing an enormous amount of growth of the people that consume data and also measuring to see how many times better data is recommended to users to use.”

That represents a critical difference from past practices, where people used only the data available to them and treating that as the best data even if more complete, accurate, and clean data sets existed elsewhere in the company. “Every user that picks data from the data catalog gets two recommendations automatically,” he says – alternative data sets that the solution presents as potentially better to use and complementary data sets that makes sense to use with the data that’s already been chosen.

“From our perspective, there is a specific need for a catalog, whether for Analytics or empowering Data Governance processes like GDPR,” Schwartz says, noting that about 300 customers in the last year have been using it to become data-driven.

Getting Truly Customer-Focused

For Informatica’s customers, the Data Catalog is essential to truly understanding their own customers, surfacing data from anywhere that it exists to provide the discoverability they need to change business models as needed. “The Interesting part is, to do that, you need a lot of different information about the customer for a 360-degree view,” he says. Most organizations, he notes, may think they are customer-focused but still have a lot to do to really know their customers and be a step ahead in giving them what they want.

Thus, Informatica looked to Master Data not just from internal systems about customers but also as Reference Data from social media, usage of mobile apps, and third parties. “Basically, we bring this data together with organizational information to open the door for new opportunities,” expanding the information available in the Master Data set, he says.

Seamlessly and efficiently collecting that data, integrating those sources, and connecting it to Master Data makes it possible to build a strategy around getting customer information at the right time to offer them a service or product.

AI and Machine Learning are critical for using that information to gain insight about the optimized time to approach a customer about a new offering. “An AI and Machine Learning environment builds the right analysis to know how and when to recommend,” eliciting information that will trigger the offer being presented at the right time. “Before it would take too long and the opportunity would be lost,” he says. That said, keep in mind that “good Machine Learning and good AI is based on a lot of data and on data that is accurate,” Schwartz says, otherwise results are sub-optimal and misleading.

That’s a risk when the future will see more data and more users that want to use it. But while there remains a long way to go for companies to fully appreciate data access and data use, he says, they are at last starting to think about how it should be a central focus of the business.

About the author

Jennifer Zaino is a New York-based freelance writer specializing in business and technology journalism. She has been an executive editor at leading technology publications, including InformationWeek, where she spearheaded an award-winning news section, and Network Computing, where she helped develop online content strategies including review exclusives and analyst reports. Her freelance credentials include being a regular contributor of original content to The Semantic Web Blog; acting as a contributing writer to RFID Journal; and serving as executive editor at the Smart Architect Smart Enterprise Exchange group. Her work also has appeared in publications and on web sites including EdTech (K-12 and Higher Ed), Ingram Micro Channel Advisor, The CMO Site, and Federal Computer Week.